What it does
Ollama provides tools for downloading and running supported language models and accessing them through an API. It reduces the setup needed to connect a model to a local application. That makes it a practical place to explore document helpers or development tools before deciding how inference should be deployed.
Where it fits
The interesting question is whether a local model changes a real product constraint. Perhaps documents must stay on a controlled machine, or a narrow task should work without a remote inference call. Start with that requirement. Choosing a local runtime just because it is available is not an architecture decision.
What to check
Memory, model size and available hardware affect responsiveness and concurrency. Model weights can carry licenses different from the runtime. Trace the complete data path: an application can still send prompts to a hosted provider, log sensitive input or expose an API even when a local model is installed. Read the current network and cloud settings rather than assuming the word local settles those questions.
A useful first experiment
Choose a small representative task and record response time, memory use and output quality on your actual machine. Then test the application with its expected network access disabled. Compare the result with the simplest hosted alternative using the same task. This article is based on documentation, not a published hardware benchmark.